Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR

Abstract

Virtual reality (VR) telepresence applications and the so-called "metaverse" promise to be the next major medium of human-computer interaction. However, with recent studies demonstrating the ease at which VR users can be profiled and deanonymized, metaverse platforms carry many of the privacy risks of the conventional internet (and more) while at present offering few of the defensive utilities that users are accustomed to having access to. To remedy this, we present the first known method of implementing an "incognito mode" for VR. Our technique leverages local differential privacy to quantifiably obscure sensitive user data attributes, with a focus on intelligently adding noise when and where it is needed most to maximize privacy while minimizing usability impact. Our system is capable of flexibly adapting to the unique needs of each VR application to further optimize this trade-off. We implement our solution as a universal Unity (C#) plugin that we then evaluate using several popular VR applications. Upon faithfully replicating the most well-known VR privacy attack studies, we show a significant degradation of attacker capabilities when using our solution.

arXiv commentLearn more at https://rdi.berkeley.edu/metaverse/metaguard/

References in corpus (8)

• Security and Privacy Approaches in Mixed Reality: A Literature Survey
• Privacy-Aware Eye Tracking Using Differential Privacy
• A privacy-preserving approach to streaming eye-tracking data
• De-anonymisation attacks on Tor: A Survey
• Differential Privacy for Eye-Tracking Data
• Beyond Being Real: A Sensorimotor Control Perspective on Interactions in Virtual Reality
• Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
• Attack Trees for Security and Privacy in Social Virtual Reality Learning Environments

Cited by in corpus (6)

• Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy
• SoK: Data Privacy in Virtual Reality
• Berkeley Open Extended Reality Recordings 2023 (BOXRR-23): 4.7 Million Motion Capture Recordings from 105,852 Extended Reality Device Users
• Truth in Motion: The Unprecedented Risks and Opportunities of Extended Reality Motion Data
• PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy
• Movement- and Traffic-based User Identification in Commercial Virtual Reality Applications: Threats and Opportunities